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Record W4235538171 · doi:10.1097/spv.0b013e3181d95a72

Editor's Introduction

2010· article· en· W4235538171 on OpenAlexaff
Alfred E. Bent

Bibliographic record

VenueFemale Pelvic Medicine & Reconstructive Surgery · 2010
Typearticle
Languageen
FieldMedicine
TopicUreteral procedures and complications
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicinePresentation (obstetrics)Medical educationSurgery

Abstract

fetched live from OpenAlex

The 36 Annual Scientific Meeting of the Society of Gynecologic Surgeons returns to Tucson, Arizona where the usual surgical presentations are eagerly awaited. The new journal, Female Pelvic Medicine & Reconstructive Surgery, is pleased to be able to carry the abstracts in this supplement, and also has online access for retrieval and review of the abstracts. The presentations have good variety, excellent science, and a range of presentation styles. There has been significant time, thought, and effort devoted to each presentation submission. The chosen submissions have been presented in word abstract form only in this publication, ie, no tables or figures. The meeting commences with an afternoon Robotics Course. Some of the regular meeting highlights include presentations on use of mesh in vaginal surgery, considerations on the FDA notification regarding use of mesh, education of residents through skills labs, simulators, self-directed learning and portable devices, cost factors in varied surgical approaches, and sexual function after a number of surgical procedures. Preventive strategies are presented for DVT and brachial plexus injuries. Videos are again a major part of the program and include tips on difficult vaginal hysterectomy, several presentations on robotic surgery, fistula repair, and cystoscopic findings. Many papers do not make the podium and numerous interesting procedures, observations, and learning tips are included in the nonoral poster presentations. While it is hoped you attend all the presentations in person, the abstracts can be a guide to what to expect, and a reminder of what was said. It can be searched later on for that all important presentation with a key piece of new information. Once again, we thank those who have done the research, prepared their work diligently, and shared it with the rest of us. It is a continual process of question and answer, so be prepared to challenge the presenters with your questions, and you will then have answers for the next questioner.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.272
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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